A tooth the resolver refuses to guess at is currently a dead end: the sheet
says the quadrant was missing and the clinician has to leave and find the
tooth on the chart. But the readings are enumerable — "دو" is one of four
teeth, "دو بالا" one of two — so unresolved items now carry them.
Narrowed by whatever was actually said, so this stays a choice offered to
the clinician rather than a guess made for them. Only tooth_missing_quadrant
carries candidates; a wrong position or a deciduous tooth has nothing to
choose between.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The extraction model transcribes Persian speech, so it can hand back "۲۶"
in Persian digits or "2 6" from a digit-by-digit dictation. Both were
compared literally against /^[1-8][1-8]$/, missed, and fell through to the
positional branch with no quadrant — where the tooth was reported as "not
understood". The clinician loses a tooth and is told the words were the
problem.
normalizeFdiCode() now runs at both the branch choice and the final
validation, so the two cannot disagree. toLatinDigits moves out of
jalali.ts into common/digits.ts: it was exported but unused in production,
and a tooth module reaching into the calendar module would read as an
accident.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
"ترمیم برای دندون دو" set the treatment type but reported the tooth as
unreadable. Nothing was misheard: position 2 arrived intact, with no
quadrant, because none was spoken — four teeth carry position 2 and the
resolver correctly refused to pick one. Only the label was wrong, and it
sent the clinician looking for a transcription fault.
Adds a tooth_missing_quadrant reason that names what is missing and shows
how to say it ("دو بالا راست"), and tells the model explicitly to report a
quadrant-less number with arch and side null rather than guessing.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The extraction model emits intents, never resolved values — no FDI codes, no
ISO dates. This adds the contract it must satisfy and the resolver that turns
spoken tooth references into FDI, so quadrant mirroring is a unit test rather
than a hope.
resolveToothIntent never guesses and never clamps: position 9, a deciduous
tooth, or a malformed shape resolve to null and are reported as unresolved with
the transcript span that produced them, so the review sheet can show the
clinician exactly which words were not understood.
Everything here parses untrusted model output, so nothing may throw:
- a non-array where a list was expected degrades like any other malformed shape
- explicit codes are trimmed, for parity with normalizeTeeth
- '51' reports as not_permanent_tooth (a real primary tooth the chart cannot
show) while '99' reports as malformed — the clinician should not be told a
deciduous tooth was heard when nothing tooth-shaped was
- unresolved items only dedupe when they carry a spoken span; without one,
collapsing them would hide a lost tooth behind a single blank review row
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
